Triaxial Accelerometer-Based Falls and Activities of Daily Life Detection Using Machine Learning

Althobaiti, Turke; Katsigiannis, Stamos; Ramzan, Naeem

doi:10.3390/s20133777

Triaxial Accelerometer-Based Falls and Activities of Daily Life Detection Using Machine Learning

Althobaiti, Turke; Katsigiannis, Stamos; Ramzan, Naeem

Authors

Turke Althobaiti

Dr Stamos Katsigiannis stamos.katsigiannis@durham.ac.uk
Assistant Professor

Naeem Ramzan

Abstract

The detection of activities of daily living (ADL) and the detection of falls is of utmost importance for addressing the issue of serious injuries and death as a consequence of elderly people falling. Wearable sensors can provide a viable solution for monitoring people in danger of falls with minimal external involvement from health or care home workers. In this work, we recorded accelerometer data from 35 healthy individuals performing various ADLs, as well as falls. Spatial and frequency domain features were extracted and used for the training of machine learning models with the aim of distinguishing between fall and no fall events, as well as between falls and other ADLs. Supervised classification experiments demonstrated the efficiency of the proposed approach, achieving an F1-score of 98.41% for distinguishing between fall and no fall events, and an F1-score of 88.11% for distinguishing between various ADLs, including falls. Furthermore, the created dataset, named “ShimFall&ADL” will be publicly released to facilitate further research on the field.

Citation

Althobaiti, T., Katsigiannis, S., & Ramzan, N. (2020). Triaxial Accelerometer-Based Falls and Activities of Daily Life Detection Using Machine Learning. Sensors, 20(13), Article 3777. https://doi.org/10.3390/s20133777

Journal Article Type	Article
Acceptance Date	Jul 1, 2020
Online Publication Date	Jul 6, 2020
Publication Date	2020-07
Deposit Date	Sep 15, 2020
Publicly Available Date	Sep 18, 2020
Journal	Sensors
Publisher	MDPI
Peer Reviewed	Peer Reviewed
Volume	20
Issue	13
Article Number	3777
DOI	https://doi.org/10.3390/s20133777

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© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).